Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia

Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-b...

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Autores principales:	Taimi S. Kapalanga, Zvikomborero Hoko, Webster Gumindoga, Loyd Chikwiramakomo
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	landsat 8 reflectance values regression algorithms remote sensing retrieval water quality Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/f4d3e3d168384441bba9da327b7f9b5f
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spelling	oai:doaj.org-article:f4d3e3d168384441bba9da327b7f9b5f2021-11-06T07:17:58ZRemote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia1606-97491607-079810.2166/ws.2020.290https://doaj.org/article/f4d3e3d168384441bba9da327b7f9b5f2021-08-01T00:00:00Zhttp://ws.iwaponline.com/content/21/5/1878https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally the best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality. HIGHLIGHTS Over past years, frequent and continuous water quality monitoring has been problematic in Namibia.; A linear regression can now be used to develop algorithms for retrieving water quality data.; Good prediction accuracy for turbidity, TN, TP and total algae count.; More sampling points needed to further improve regression model accuracy.; Remote sensing provides rapid information on water quality spatio-temporal variability.;Taimi S. KapalangaZvikomborero HokoWebster GumindogaLoyd ChikwiramakomoIWA Publishingarticlelandsat 8reflectance valuesregression algorithmsremote sensingretrievalwater qualityWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 5, Pp 1878-1894 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	landsat 8 reflectance values regression algorithms remote sensing retrieval water quality Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	landsat 8 reflectance values regression algorithms remote sensing retrieval water quality Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Taimi S. Kapalanga Zvikomborero Hoko Webster Gumindoga Loyd Chikwiramakomo Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
description	Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally the best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality. HIGHLIGHTS Over past years, frequent and continuous water quality monitoring has been problematic in Namibia.; A linear regression can now be used to develop algorithms for retrieving water quality data.; Good prediction accuracy for turbidity, TN, TP and total algae count.; More sampling points needed to further improve regression model accuracy.; Remote sensing provides rapid information on water quality spatio-temporal variability.;
format	article
author	Taimi S. Kapalanga Zvikomborero Hoko Webster Gumindoga Loyd Chikwiramakomo
author_facet	Taimi S. Kapalanga Zvikomborero Hoko Webster Gumindoga Loyd Chikwiramakomo
author_sort	Taimi S. Kapalanga
title	Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
title_short	Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
title_full	Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
title_fullStr	Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
title_full_unstemmed	Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia
title_sort	remote-sensing-based algorithms for water quality monitoring in olushandja dam, north-central namibia
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/f4d3e3d168384441bba9da327b7f9b5f
work_keys_str_mv	AT taimiskapalanga remotesensingbasedalgorithmsforwaterqualitymonitoringinolushandjadamnorthcentralnamibia AT zvikomborerohoko remotesensingbasedalgorithmsforwaterqualitymonitoringinolushandjadamnorthcentralnamibia AT webstergumindoga remotesensingbasedalgorithmsforwaterqualitymonitoringinolushandjadamnorthcentralnamibia AT loydchikwiramakomo remotesensingbasedalgorithmsforwaterqualitymonitoringinolushandjadamnorthcentralnamibia
_version_	1718443831382245376

Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia

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